Logistic regression with two explanatory variables

Logistic regression also supports multiple explanatory variables. To include multiple explanatory variables in logistic regression models, the syntax is the same as for linear regressions.

Here you'll fit a model of churn status with both of the explanatory variables from the dataset: the length of customer relationship and the recency of purchase, and their interaction.

churn is available.

This exercise is part of the course

Intermediate Regression with statsmodels in Python

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Exercise instructions

Import the logit() function from statsmodels.formula.api.
Fit a logistic regression of churn status, has_churned, versus length of customer relationship, time_since_first_purchase, and recency of purchase, time_since_last_purchase, and an interaction between the explanatory variables.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import logit
____

# Fit a logistic regression of churn status vs. length of relationship, recency, and an interaction
mdl_churn_vs_both_inter = ____

# Print the coefficients
print(mdl_churn_vs_both_inter.params)

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